A projection-based model reduction enables exponential state-space reduction for constrained quantum optimization applied to random 3-SAT and agent coordination on graphs.
Constraint Preserving Mixers for the Quantum Approximate Optimization Algorithm
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
fields
quant-ph 3years
2026 3verdicts
UNVERDICTED 3representative citing papers
A qubit-efficient colored-permutation encoding for CVRP enables Constraint-Enhanced QAOA to recover verified optimal solutions on benchmarks without additional capacity qubits.
CE-QAOA with finite layers achieves dimension-free success probability bounds q0 ≥ x/(1+x) via Fejér filtering under a wrapped phase-separation condition.
citing papers explorer
-
Constrained Quantum Optimization meets Model Reduction
A projection-based model reduction enables exponential state-space reduction for constrained quantum optimization applied to random 3-SAT and agent coordination on graphs.
-
Optimal, Qubit-Efficient Quantum Vehicle Routing via Colored-Permutations
A qubit-efficient colored-permutation encoding for CVRP enables Constraint-Enhanced QAOA to recover verified optimal solutions on benchmarks without additional capacity qubits.
-
Finite-Depth, Finite-Shot Guarantees for Constrained Quantum Optimization via Fej\'er Filtering
CE-QAOA with finite layers achieves dimension-free success probability bounds q0 ≥ x/(1+x) via Fejér filtering under a wrapped phase-separation condition.